This is my first data science and machine leearning project. i am using the titanic dataset from kaggle.¶

I want to do the titanic survival prediction project¶

so Help me God¶

In [1]:
#importing libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
In [2]:
#loading the dataset
df=pd.read_csv('Titanic.csv')
In [3]:
#displaying the first five columns of the data
df.head()
Out[3]:
sex age sibsp parch fare embarked class who alone survived
0 male 22.0 1 0 7.2500 S Third man False 0
1 female 38.0 1 0 71.2833 C First woman False 1
2 female 26.0 0 0 7.9250 S Third woman True 1
3 female 35.0 1 0 53.1000 S First woman False 1
4 male 35.0 0 0 8.0500 S Third man True 0
In [4]:
#displaying the last five columns of the data
df.tail()
Out[4]:
sex age sibsp parch fare embarked class who alone survived
886 male 27.0 0 0 13.00 S Second man True 0
887 female 19.0 0 0 30.00 S First woman True 1
888 female NaN 1 2 23.45 S Third woman False 0
889 male 26.0 0 0 30.00 C First man True 1
890 male 32.0 0 0 7.75 Q Third man True 0
In [5]:
#getting the no of rows in our data
len(df)
Out[5]:
891
In [6]:
#getting the columns in our data
df.columns
Out[6]:
Index(['sex', 'age', 'sibsp', 'parch', 'fare', 'embarked', 'class', 'who',
       'alone', 'survived'],
      dtype='object')

diplaying the number of values in each sample class

In [7]:
df['age'].value_counts()
Out[7]:
age
24.00    30
22.00    27
18.00    26
19.00    25
28.00    25
         ..
36.50     1
55.50     1
0.92      1
23.50     1
74.00     1
Name: count, Length: 88, dtype: int64
In [8]:
df['fare'].value_counts()
Out[8]:
fare
8.0500     43
13.0000    42
7.8958     38
7.7500     34
26.0000    31
           ..
35.0000     1
28.5000     1
6.2375      1
14.0000     1
10.5167     1
Name: count, Length: 248, dtype: int64
In [9]:
df['who'].value_counts()
Out[9]:
who
man      537
woman    271
child     83
Name: count, dtype: int64
In [10]:
df['alone'].value_counts()
Out[10]:
alone
True     537
False    354
Name: count, dtype: int64
In [11]:
df['class'].value_counts()
Out[11]:
class
Third     491
First     216
Second    184
Name: count, dtype: int64
In [12]:
df['alone'].value_counts()
Out[12]:
alone
True     537
False    354
Name: count, dtype: int64
In [13]:
df.describe()
Out[13]:
age sibsp parch fare survived
count 714.000000 891.000000 891.000000 891.000000 891.000000
mean 29.699118 0.523008 0.381594 32.204208 0.383838
std 14.526497 1.102743 0.806057 49.693429 0.486592
min 0.420000 0.000000 0.000000 0.000000 0.000000
25% 20.125000 0.000000 0.000000 7.910400 0.000000
50% 28.000000 0.000000 0.000000 14.454200 0.000000
75% 38.000000 1.000000 0.000000 31.000000 1.000000
max 80.000000 8.000000 6.000000 512.329200 1.000000
In [14]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 10 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   sex       891 non-null    object 
 1   age       714 non-null    float64
 2   sibsp     891 non-null    int64  
 3   parch     891 non-null    int64  
 4   fare      891 non-null    float64
 5   embarked  889 non-null    object 
 6   class     891 non-null    object 
 7   who       891 non-null    object 
 8   alone     891 non-null    bool   
 9   survived  891 non-null    int64  
dtypes: bool(1), float64(2), int64(3), object(4)
memory usage: 63.6+ KB
In [15]:
df.isnull().sum()
Out[15]:
sex           0
age         177
sibsp         0
parch         0
fare          0
embarked      2
class         0
who           0
alone         0
survived      0
dtype: int64

VISUALIZATION 1.HISTOGRAMS

In [16]:
df['age'].hist()
Out[16]:
<Axes: >
No description has been provided for this image
In [17]:
df['who'].hist()
Out[17]:
<Axes: >
No description has been provided for this image
In [18]:
df['fare'].hist()
Out[18]:
<Axes: >
No description has been provided for this image
In [19]:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
In [20]:
df.shape
Out[20]:
(891, 10)
In [21]:
df['sex']=le.fit_transform(df['sex'])
df.head()
Out[21]:
sex age sibsp parch fare embarked class who alone survived
0 1 22.0 1 0 7.2500 S Third man False 0
1 0 38.0 1 0 71.2833 C First woman False 1
2 0 26.0 0 0 7.9250 S Third woman True 1
3 0 35.0 1 0 53.1000 S First woman False 1
4 1 35.0 0 0 8.0500 S Third man True 0
In [22]:
df['who']=le.fit_transform(df['who'])
df.head()
df.tail()
Out[22]:
sex age sibsp parch fare embarked class who alone survived
886 1 27.0 0 0 13.00 S Second 1 True 0
887 0 19.0 0 0 30.00 S First 2 True 1
888 0 NaN 1 2 23.45 S Third 2 False 0
889 1 26.0 0 0 30.00 C First 1 True 1
890 1 32.0 0 0 7.75 Q Third 1 True 0
In [23]:
df['alone']=le.fit_transform(df['alone'])
df.head()
Out[23]:
sex age sibsp parch fare embarked class who alone survived
0 1 22.0 1 0 7.2500 S Third 1 0 0
1 0 38.0 1 0 71.2833 C First 2 0 1
2 0 26.0 0 0 7.9250 S Third 2 1 1
3 0 35.0 1 0 53.1000 S First 2 0 1
4 1 35.0 0 0 8.0500 S Third 1 1 0
In [24]:
df['class']=le.fit_transform(df['class'])
df.head()
Out[24]:
sex age sibsp parch fare embarked class who alone survived
0 1 22.0 1 0 7.2500 S 2 1 0 0
1 0 38.0 1 0 71.2833 C 0 2 0 1
2 0 26.0 0 0 7.9250 S 2 2 1 1
3 0 35.0 1 0 53.1000 S 0 2 0 1
4 1 35.0 0 0 8.0500 S 2 1 1 0
In [25]:
df['embarked']=le.fit_transform(df['embarked'])
df.head()
Out[25]:
sex age sibsp parch fare embarked class who alone survived
0 1 22.0 1 0 7.2500 2 2 1 0 0
1 0 38.0 1 0 71.2833 0 0 2 0 1
2 0 26.0 0 0 7.9250 2 2 2 1 1
3 0 35.0 1 0 53.1000 2 0 2 0 1
4 1 35.0 0 0 8.0500 2 2 1 1 0
In [26]:
df.corr()
Out[26]:
sex age sibsp parch fare embarked class who alone survived
sex 1.000000 0.093254 -0.114631 -0.245489 -0.182333 0.104057 0.131900 -0.639773 0.303646 -0.543351
age 0.093254 1.000000 -0.308247 -0.189119 0.096067 -0.025252 -0.369226 0.378685 0.198270 -0.077221
sibsp -0.114631 -0.308247 1.000000 0.414838 0.159651 0.066654 0.083081 -0.136003 -0.584471 -0.035322
parch -0.245489 -0.189119 0.414838 1.000000 0.216225 0.038322 0.018443 -0.055682 -0.583398 0.081629
fare -0.182333 0.096067 0.159651 0.216225 1.000000 -0.221226 -0.549500 0.146290 -0.271832 0.257307
embarked 0.104057 -0.025252 0.066654 0.038322 -0.221226 1.000000 0.157112 -0.060177 0.065610 -0.163517
class 0.131900 -0.369226 0.083081 0.018443 -0.549500 0.157112 1.000000 -0.196793 0.135207 -0.338481
who -0.639773 0.378685 -0.136003 -0.055682 0.146290 -0.060177 -0.196793 1.000000 0.006540 0.325753
alone 0.303646 0.198270 -0.584471 -0.583398 -0.271832 0.065610 0.135207 0.006540 1.000000 -0.203367
survived -0.543351 -0.077221 -0.035322 0.081629 0.257307 -0.163517 -0.338481 0.325753 -0.203367 1.000000
In [27]:
corr=df.corr()
fig, ax= plt.subplots(figsize=(10,10))
sns.heatmap(corr, annot =True, ax=ax,cmap='coolwarm')
Out[27]:
<Axes: >
No description has been provided for this image
In [28]:
#scatterplots
sns.pairplot(df,hue='survived')
plt.show()
No description has been provided for this image
In [29]:
sns.boxplot(y='age', data=df,width=0.2)
Out[29]:
<Axes: ylabel='age'>
No description has been provided for this image
In [30]:
sns.boxplot(y='age',x='sex',hue='survived', data=df,width=0.2)
Out[30]:
<Axes: xlabel='sex', ylabel='age'>
No description has been provided for this image
In [31]:
df.head(10)
Out[31]:
sex age sibsp parch fare embarked class who alone survived
0 1 22.0 1 0 7.2500 2 2 1 0 0
1 0 38.0 1 0 71.2833 0 0 2 0 1
2 0 26.0 0 0 7.9250 2 2 2 1 1
3 0 35.0 1 0 53.1000 2 0 2 0 1
4 1 35.0 0 0 8.0500 2 2 1 1 0
5 1 NaN 0 0 8.4583 1 2 1 1 0
6 1 54.0 0 0 51.8625 2 0 1 1 0
7 1 2.0 3 1 21.0750 2 2 0 0 0
8 0 27.0 0 2 11.1333 2 2 2 0 1
9 0 14.0 1 0 30.0708 0 1 0 0 1
In [33]:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
x_train = imputer.fit_transform(x_train)
x_test = imputer.transform(x_test)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[33], line 3
      1 from sklearn.impute import SimpleImputer
      2 imputer = SimpleImputer(strategy='mean')
----> 3 x_train = imputer.fit_transform(x_train)
      4 x_test = imputer.transform(x_test)

NameError: name 'x_train' is not defined

MODEL TRAINING

In [34]:
from sklearn.model_selection import train_test_split
X=df.drop(columns=['survived'])
Y=df['survived']
x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=42)
In [35]:
print(y_train.unique())  # If using pandas Series

[0 1]
In [36]:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
x_train = imputer.fit_transform(x_train)
x_test = imputer.transform(x_test)
In [37]:
#LOGISTIC REGRESSION
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(x_train, y_train)

C:\Users\Win\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\linear_model\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(
Out[37]:
LogisticRegression()
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LogisticRegression()

Print metric to get performance

In [38]:
print("Accuracy:",model.score(x_test,y_test)*100)

Accuracy: 79.88826815642457
In [39]:
#K-NEAREST NEIGHBORS
from sklearn.neighbors import KNeighborsClassifier
model=KNeighborsClassifier()
model.fit(x_train,y_train)
Out[39]:
KNeighborsClassifier()
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KNeighborsClassifier()
In [40]:
print("Accuracy:",model.score(x_test,y_test)*100)

Accuracy: 71.50837988826815
In [41]:
#DECISION TREE
from sklearn.tree import DecisionTreeClassifier
model=DecisionTreeClassifier()
model.fit(x_train,y_train)
Out[41]:
DecisionTreeClassifier()
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DecisionTreeClassifier()
In [42]:
print("Accuracy:",model.score(x_test,y_test)*100)

Accuracy: 79.3296089385475
In [43]:
from sklearn.ensemble import RandomForestClassifier
model=RandomForestClassifier()
model.fit(x_train,y_train)
Out[43]:
RandomForestClassifier()
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RandomForestClassifier()
In [44]:
print("Accuracy:",model.score(x_test,y_test)*100)

Accuracy: 82.12290502793296
In [45]:
#use a trained model to make predict
print(x_test[:5])  # Show first 5 rows of the NumPy array

[[ 1.         29.49884615  1.          1.         15.2458      0.
   2.          1.          0.        ]
 [ 1.         31.          0.          0.         10.5         2.
   1.          1.          1.        ]
 [ 1.         20.          0.          0.          7.925       2.
   2.          1.          1.        ]
 [ 0.          6.          0.          1.         33.          2.
   1.          0.          0.        ]
 [ 0.         14.          1.          0.         11.2417      0.
   2.          0.          0.        ]]
In [46]:
import pandas as pd

# You need to know the column names — assuming X.columns from the original DataFrame
x_test_df = pd.DataFrame(x_test, columns=X.columns)
x_test_df.head()
Out[46]:
sex age sibsp parch fare embarked class who alone
0 1.0 29.498846 1.0 1.0 15.2458 0.0 2.0 1.0 0.0
1 1.0 31.000000 0.0 0.0 10.5000 2.0 1.0 1.0 1.0
2 1.0 20.000000 0.0 0.0 7.9250 2.0 2.0 1.0 1.0
3 0.0 6.000000 0.0 1.0 33.0000 2.0 1.0 0.0 0.0
4 0.0 14.000000 1.0 0.0 11.2417 0.0 2.0 0.0 0.0
In [47]:
y_test.head()
Out[47]:
709    1
439    0
840    0
720    1
39     1
Name: survived, dtype: int64
In [48]:
clf= RandomForestClassifier()
clf.fit(x_train,y_train)
(clf.score(x_test,y_test)*100)
Out[48]:
82.12290502793296
In [49]:
clf.predict(x_test)
Out[49]:
array([0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1,
       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1,
       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0,
       0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1,
       0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
       0, 1, 1], dtype=int64)
In [50]:
np.array(y_test)
Out[50]:
array([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,
       1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0,
       0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
       0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1,
       1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
       0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,
       0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0,
       1, 1, 1], dtype=int64)
In [51]:
#compare the prediction to the truth labels to evaluate the model
y_preds=clf.predict(x_test)
(np.mean(y_preds == y_test)*100)
Out[51]:
82.12290502793296
In [52]:
pip install plotly

Requirement already satisfied: plotly in c:\users\win\appdata\local\programs\python\python312\lib\site-packages (6.1.2)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\win\appdata\local\programs\python\python312\lib\site-packages (from plotly) (1.43.0)
Requirement already satisfied: packaging in c:\users\win\appdata\local\programs\python\python312\lib\site-packages (from plotly) (23.2)
Note: you may need to restart the kernel to use updated packages.

[notice] A new release of pip is available: 24.0 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
In [53]:
import plotly.express as px
import plotly.io as pio
pio.renderers.default = 'notebook'  # or 'iframe', 'colab', or 'browser' if needed


fig = px.bar(df, x='age', y='sex', color='survived')
fig.show()
In [61]:
df.plot()
plt.title("a general plot for the dataframe")
plt.show()
No description has been provided for this image
In [60]:
df.plot(kind='scatter',x='age',y='class')
plt.title("class vs age")
plt.xlabel("age")
plt.ylabel("class")
plt.show()
No description has been provided for this image
In [ ]: